图模型选择的决策理论基础
Decision Theoretic Foundations of Graphical Model Selection
摘要：
本文描述了一个决策理论公式学习的图形结构的贝叶斯信念网络的数据。这个框架包含了标准的贝叶斯方法，选择具有最大后验概率的模型作为具有0-1损失函数的决策问题的解决方案，并允许使用更一般的损失函数，能够权衡所选模型的复杂性和选择过度简化模型的错误。引入了一类新的损失函数，称为可分解的，以使决策问题与图模型的可分解性相匹配。利用这类损失函数，可以使用有效的自底向上搜索策略找到决策问题的最优解。
英文摘要：
This paper describes a decision theoretic formulation of learning the graphical structure of a Bayesian Belief Network from data. This framework subsumes the standard Bayesian approach of choosing the model with the largest posterior probability as the solution of a decision problem with a 0-1 loss function and allows the use of more general loss functions able to trade-off the complexity of the selected model and the error of choosing an oversimplified model. A new class of loss functions, called disintegrable, is introduced, to allow the decision problem to match the decomposability of the graphical model. With this class of loss functions, the optimal solution to the decision problem can be found using an efficient bottom-up search strategy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
